Big data industry decision is supremely important for companies to boost the efficiency of leadership, which can vastly accelerate industrialized. With regard to big data industry decision assessment, the intrinsic problem involves enormous inexactness, fuzziness and ambiguity. Pythagorean fuzzy sets (PFSs), managing the uncertainness depicted in non-membership with membership, are a quite practical way to capture uncertainness. Firstly, the innovative Pythagorean fuzzy score function is given to dispose the comparison issue. Innovative distance measure and similarity measure for PFSs with three parameters are explored, along with corresponding proofs therewith. Later, objective weight is ascertained by deviation-based method. Also, combined weight is skillfully designed, which can tellingly imply both subjective preference and objective preference. In addition, an approach to settle Pythagorean fuzzy problem by multiparametric similarity measure is presented. The efficacy of developed algorithm is elaborated by a big data industry decision issue. Moreover, a comparison of the introduced algorithm with the selected existing methods has been built on the basis of the division by zero issue and counterintuitive phenomena for displaying its effectiveness.

大数据行业的决策对于公司提高领导效率至关重要，这可以极大地加快工业化进程。关于大数据行业决策评估，内在问题涉及巨大的不精确性，模糊性和模糊性。毕达哥拉斯模糊集（PFS）处理具有成员资格的非成员身份中描述的不确定性是捕获不确定性的一种非常实用的方法。首先，给出了新颖的毕达哥拉斯模糊得分函数来处理比较问题。探索了具有三个参数的PFS的创新距离度量和相似度量，以及相应的证明。之后，通过基于偏差的方法确定目标权重。此外，组合重量也经过精心设计，这可以明显地暗示主观偏好和客观偏好。此外，提出了一种通过多参数相似度度量解决勾股模糊问题的方法。大数据行业决策问题阐述了开发算法的有效性。此外，在除以零问题和违反直觉的现象的基础上，将引入的算法与选定的现有方法进行了比较，以显示其有效性。